Panmodernism by Mark Bloch

As collected from the Internet, mostly from
Wikipedia, with humility and profound
gratitude with no claims of ownership,
except for the Panmodern concept itself,
which, if you read all of this as I have,
you will vaguely understand. I am
thankful for all the authors of this
material and their ideas. All rights
reserved, all wrongs
reversed.

Diffusion of
innovations

From
Wikipedia, the free encyclopedia

Diffusion of
innovation is a theory of how, why, and at what rate new ideas and technology spread through cultures. Everett Rogers introduced it in his 1962 book, Diffusion
of Innovations, writing that "Diffusion is the process by which an innovation is
communicated through certain channels over time among the members of a social
system."[1]

Contents

The S-Curve and technology adoption

The
adoption curve becomes an s-curve when cumulative adoption is used.

Rogers theorized
that innovations would spread through a community in an S
curve,[2] as the early adopters select the innovation (which may
be a technology) first, followed by the majority, until a technology or
innovation has reached its saturation point in a community.

According to
Rogers, diffusion research centers on the conditions which increase or decrease
the likelihood that a new idea, product, or practice will be adopted by members
of a given culture. According to Rogers people’s attitude toward a new
technology is a key element in its diffusion. Roger’s Innovation Decision
Process theory proposes that innovation adoption is a process that occurs over
time through five stages: Knowledge, Persuasion, Decision, Implementation and
Confirmation. Accordingly, the innovation-decision process is the process
through which an individual or other decision-making unit passes 1. from first
knowledge of an innovation, 2. to forming an attitude toward the innovation, 3.
to a decision to adopt or reject, 4. to implementation of the new idea, and 5.
to confirmation of this decision.[3]

Much of the
evidence for the diffusion of innovations gathered by Rogers comes from
agricultural methods and medical practice.

Various computer
models have been developed in order to simulate the diffusion of innovations.
Veneris[4][5] developed a systems
dynamics computer model which takes into account various diffusion patterns
modeled via differential equations.

There are a
number of criticisms of the model which make it less than useful for managers.
First, that technologies are not static, there is continual innovation in order
to attract new adopters all along the S-curve, the S-curve does not just
'happen'. Instead, the s-curve can be seen as being made up a series of 'bell
curves' of different sections of a population adopting different versions of a
generic innovation.

Early
life

Rogers was born
on the family Pinehurst Farm in Carroll, Iowa, in 1931. His
father loved electromechanical farm innovations, but was highly resistant to
biological–chemical innovations, so he resisted adopting the new hybrid
seed corn, even though it yielded 25% more crop and was resistant to drought.
During the Iowa drought of 1936, while the hybrid seed corn stood tall on the
neighbor’s farm, however, the crop on the Rogers’ farm wilted. Rogers’ father
was finally convinced.

Rogers had no
plans to attend university until a school teacher drove him and some classmates
to Ames to visit Iowa State University. Rogers decided to
pursue a degree in agriculture there. He then served in the Korean War for two
years. He returned to Iowa State University to earn a Ph.D. in sociology and
statistics in 1957.

Academic
research

Iowa State
University in those years (the 1950s) had a great intellectual tradition in
agriculture and in rural sociology. Numerous agricultural innovations were
generated by scientists at land grant universities and at the U.S. Department
of Agriculture. Rural sociologists, including Rogers’ doctoral advisor George
Beal, were conducting pioneering studies on the diffusion of these innovations,
like the high-yielding hybrid seed corn, chemical fertilizers, and weed sprays.
Questions were being asked about why some farmers adopted these innovations
while others did not, and also about why it takes such a long time for these
seemingly advantageous innovations to diffuse.

These questions
about innovation diffusion, including the strong resistances and how they could
be overcome, formed the core of Rogers' graduate work at ISU. His doctoral
dissertation was a study of the diffusion of weed spray, and involved
interviewing more than 200 farmers about their adoption decisions.

He also reviewed
existing studies of the diffusion of all kinds of
innovations—agricultural,educational, medical, marketing, and so on. He
found several similarities in these diverse studies. For instance, innovations
tend to diffuse following an S-curve of adoption.

In 1962, Rogers
publishes this review of literature chapter, greatly expanded, enhanced, and
refined, as the now-legendary book Diffusion of Innovations. The book provided
a comprehensive theory of how innovations diffused, or spread, in a social
system. The book’s appeal was global. Its timing was uncanny. National
governments in countries of Asia, Africa, and Latin America were wrestling with
how to diffuse agricultural, family planning, and other social change
innovations in their newly independent countries. Here was a theory that was
useful.

Diffusion
of Innovations

When the first
edition (1962) of Diffusion of Innovations was published, Rogers was an
assistant professor of rural sociology at Ohio State University. He was only 30
years old but was becoming a world-renowned academic figure. In the mid-2000s, The
Diffusion of Innovations is the second-most-cited book in the social sciences.

Rogers proposes
that adopters of any new innovation or idea can be categorized as innovators
(2.5%), early adopters (13.5%), early majority (34%), late
majority (34%) and laggards (16%), based on the mathematically-based Bell curve.
These categories, based on standard deviations from the mean of the normal
curve, provide a common language for innovation researchers. Each adopter's
willingness and ability to adopt an innovation depends on their awareness,
interest, evaluation, trial, and adoption. People can fall into different
categories for different innovations -- a farmer might be an early adopter
mechanical innovations, but a late majority adopter of biological innovations
or VCRs.

When graphed, the
rate of adoption formed what came to typify the DOI model, an “s-shaped curve.”
(S curve) The graph essentially shows a cumulative
percentage of adopters over time – slow at the start, more rapid as
adoption increases, then leveling off until only a small percentage of laggards
have not adopted. (Rogers Diffusion Of Innovations 1983)

To commemorate
his contributions to the field, the University of Southern California Annenberg Norman Lear Center established the Everett M. Rogers Award for
Achievement in Entertainment-Education, which recognizes outstanding practice
or research in the field of entertainment education. [3]

Later
life

In 1995, Rogers
moved to the University of New Mexico, having become fond of Albuquerque while
stationed at an airbase during the Korean War. He helped UNM launch a doctoral
program in communication. He was Distinguished Professor Emeritus at UNM.

Rogers suffered
from kidney disease and retired from UNM in the summer of 2004. He died just a
few months later, survived by his wife, Dr. Corinne Shefner-Rogers, and two
sons: David Rogers and Everett King.

When we make assumptions
about what a term means, we end up applying solutions with no relationship to
reality. The term "Early Adopter" has lost all meaning in the field
of Change Management and is causing more problems than it solves. That's a
pity, because it, and the terms surrounding it, arose from good research and
when used properly can aid in our understanding of the Change Process.

Everett Rogers, in his classic tome, "Diffusion of Innovations"
examined the "adoption levels over time" curves of hundreds of
different innovations. He noticed they were mostly the standard Bell curve. He
then, for the sake of discussion, identified different sections of this curve.
The left most 2.5% of the curve he labeled as "Innovators".

The next 13.5% were tagged as "Early Adopters".

The left of centre 34% were the "Early Majority"

The right of centre 34% are the "Late Majority" followed by the last
16%, whom he saddled with the term, "Laggards"

Once he had these categories, he examined the people residing in them to see if
he could identify common denominators beyond their location on the curve. For
example: He observed that Early Adopters were perceived as opinion leaders of
the community with respect to that change/innovation.

It's important to realize these categories had a purely statistical meaning.
The Early and Late majorities make up the core 68% of the curve as defined by
the 1st standard deviation. The Early Adopters are the left portion of the 2nd
standard deviation. In other words, "Early
Adopter" as originally intended, is purely a mathematical definition based
on the adoption curve for a particular innovation1

It's also necessary to note that this adoption curve only
exists after a population has adopted a technology2.

And finally, Adoption Curves do not exist outside
the social dynamics surrounding a specific innovation3.
ie. The same population will generate different adoption curves, if any, for a
different change/innovation.

If we lose sight of these three points, we end up abusing everything that
Diffusion Theory can teach us.

1) The statement "She is an Early Adopter" is meaningless until
associated with a specific change or innovation.

I owned a PC in 1979, which defines me at least as an Early Adopter. However, I
have only just recently (July 2004) acquired a cell phone, which makes me a
Laggard of the highest order.

The point is -- there is no contradiction here. With respect to PCs I was an
Early Adopter and with respect to Cell Phones I am a Laggard. No contradiction
exists if we use the terms
properly.

Lesson: People do not fall into one Change
Adoption Category; they drift from category to category depending on the specific change/innovation.

2) The statement "13.5% of the general population are Early
Adopters" is absolutely, totally, incorrect.

This makes two related and dangerous assumptions.
a. It assumes that
the complete Adoption Curve will
exist for any change..
b. It assumes
13.5% of us will embrace any change,

Evidence of the incorrectness of this statement is found in two casual
observations;
a) At the height of the
Hula Hoop craze, not everyone was
hula-hooping.
b) Not even 2.5% of the population have bought a Segway.

Lesson: The
adoption terms are accurate only in
hindsight; they tell you nothing about how a population might respond to a change/innovation.

"Early Adopter" and the other descriptors Rogers used to sub-divide
the Adoption Curve are post facto definitions.
They are applicable only after the
population in question has embraced a change/innovation. Just because people
take to a change/innovation before others does not mean they are "opinion leaders", that is only true if everyone else has followed their lead. Until
this happens they are merely "first to adopt" and if no-one else
follows them, then a more correct label might be "Gullible" and not
"Early Adopter".

Logistic function

Standard
logistic sigmoid function

A logistic
function or logistic curve is the most common sigmoid curve. It models the S-curve of growth of
some set[1]P, where P might be thought of as population. The initial stage of
growth is approximately exponential; then, as saturation begins, the growth slows,
and at maturity, growth stops.

where the
variable[3]P might be considered to denote a population and the variable t might be thought of as time. If we now let t range over the real
numbers from to then we obtain the S-curve
shown. In practice, due to the nature of the exponential functione− t, it is sufficient to compute t over a small range of
real numbers such as [ − 6, + 6].

Bass diffusion model

The Bass
diffusion model was developed by Frank Bass and describes the process of how new products
get adopted as an interaction between users and potential users. It has been
described as one of the most famous empirical generalisations in marketing,
along with the Dirichlet
model of repeat buying and brand choice [1]. The model is widely used in forecasting,
especially product forecasting and technology forecasting. Mathematically, the
basic Bass diffusion is a Riccati
equation with constant coefficients.

Frank Bass
published his paper "A new product growth model for consumer
durables" in 1969. [2] Prior to this, Everett
Rogers published Diffusion of Innovations, a highly influential work that
described the different stages of product adoption. Bass contributed some
mathematical ideas to the concept. [3]

This model has
been widely influential in marketing and management science. In 2004 it was
selected as one of the ten most frequently cited papers in the 50-year history
of Management Science[4]. It was ranked number five, and the only marketing paper
in the list. It was subsequently reprinted in the December 2004 issue of Management
Science.[4]

Use
in online social networks The rapid, recent (as of early 2007) growth in online
social networks (and other virtual communities) has led to an increased
use of the Bass diffusion model. The Bass diffusion model is used to estimate
the size and growth rate of these social networks.

Development communication

Development
Communication, simply defined, is the use of communication to promote social development. More specifically, it refers
to the practice of systematically applying the processes, strategies, and
principles of communication to bring about positive social change.[1]

The practice of
development communication can be traced back to efforts undertaken in various
parts of the world during the 1940s, but the widespread application of the
concept came about because of the problems that arose in the aftermath of World War
II . The rise of the communication sciences in the 1950s saw a
recognition of the field as an academic discipline, with Daniel
Lerner, Wilbur Schramm, and Everett
Rogers being the earliest influential advocates. The term "Development
Communication" was first coined in 1972 by Nora
C. Quebral, who defines the field as

"the art
and science of human communication linked to a society's planned transformation
from a state of poverty to one of dynamic socio-economic growth that makes for
greater equity and the larger unfolding of individual potential."[2]

The theory and
practice of development communication continues to evolve today, with different
approaches and perspectives unique to the varied development contexts the field
has grown in.[3]

Development
communication is characterized by conceptual flexibility and diversity of
communication techniques used to address the problem. Some approaches in the
“tool kit” of the field include: information dissemination and education,
behavior change, social marketing, social mobilization, media
advocacy, communication for social change, and participatory development
communication.

History
of the field

The theories and
practices of development communication sprang from the many challenges and
opportunities that faced development oriented institutions in the last century.
And since these institutions existed in different contexts, different schools
of development communication have arisen in different places over time.[3]

Manyozo (2006)
suggests that the history field can be broken down into those of six different
schools of development communication, with the Bretton Woods school being the
dominant paradigm in international literature, and the other schools being the
Latin American, Indian, Los BaĖos, African,and the participatory development
communication schools.

The growing
interest for these kind of applications is also reflected in the work of the
World Bank, which is very active in promoting this field through its
Development Communication division and recently (June 2008) published the
Development Communication Sourcebook, a resource addressing the history,
concepts and practical applications in this discipline.

Lazy User Model

Lazy User
Model of Solution Selection (LUM) is a new model[1] (2007) that tries to explain how an individual selects a
solution to fulfill a need from a set of possible solution alternatives. Lazy
user model expects that a solution is selected from a set of available
solutions based on the amount of effort the solutions require from the user -
the user is supposed to select the solution that carries the least effort. The
model is applicable to a number of different types of situations, but it can be
said to be closely related to technology acceptance models.

The model draws
from earlier works on how least effort affects human behaviour in information
seeking[2] and in scaling of language.[3]

Earlier research
within the discipline of information systems especially within the topic
of technology acceptance and technology adoption is closely related to the lazy
user model.

The
model structure

The model starts
from the observation that there is a user need, i.e. it is expected that there
is a “clearly definiable, fully satisfiable want” that the user want’s
satisfied (it can also said that the user has a problem and she wants the
problem solved). So there is a place for a solution / product / service.

The user need
defines the set of possible solutions (products, service etc.) that fulfill the
user need. The basic model considers for simplicity needs that are 100%
satisfiable and services that 100% satisfy the needs. This means that only the
solutions that solve the problem are relevant. This logically means that the
need defines the possible satisfying solutions - a set of solutions (many
different products / services) that all can fulfill the user need. LUM is not
limited to looking at one solution separately.

All of the
solutions in the set that fulfill the need have their own characteristics; some
are good and suitable for the user, others unsuitable and unacceptable –
for example a if the user is in a train and want’s to know what the result from
a tennis match is right now, she may only use the types of solutions to the problem
that are available to her => the user state determines the set of available /
suitable solutions for the user and thus limits the (available) set of possible
solutions to fulfill the user need. The user state is a very wide concept, it
is the user characteristics at the time of the need. The user state includes,
e.g., age, wealth, location... everything that determines the state of the user
in relation with the solutions in the set of the possible solutions to fulfill
the user need.

The model
supposes that after the user need has defined the set of possible solutions
that fulfill the user need and the user state has limited the set to the
available plausible solutions that fulfill the user need the user will select a solution from the set
to fulfill the need. Obviously if the set is empty the user does not have a way
to fulfill the need. The lazy user model assumes that the user will make the
selection from the limited set based on the lowest level of effort. Effort is
understood as the combination of monetary cost + time needed + physical /
mental effort needed.[4]

See the Lazy User
Model Homepage for a graphical presentation of the model structure.

It builds on the
earlier work of May and his co-researchers, who initially developed a Normalization Process Model to explain
the social processes that lead to the routine embedding of innovative health
technologies. [4][5] Normalization Process Theory radically extends the scope
of the model. However, it retains its focus on agency and
incorporates its constructs of Collective Action (the work that people do to
enact a new technology) as one of four social mechanisms that are postulated to
drive implementation processes.

P1.Material practices
become routinely embedded in social contexts as the result of individual and
collective agency. From this follow specific propositions that define a mechanism (i.e.
embedding is dependent on socially patterned implementation work).

P2. The work of embedding
is operationalized through four generative mechanisms (coherence; cognitive
participation; collective action; reflexive monitoring). From this follows
specific propositions that define components of a mechanism (i.e. those factors
that shape socially patterned implementation work). Generative mechanisms are
defined thus:

Coherence (or sense-making): expressed agency
that defines and organizes the components of an implementation
process.

Cognitive Participation: expressed agency
that defines and organizes the actors involved in an
implementation process.

Collective Action: expressed agency
that defines and organizes the enacting of an implementation
process.

Reflexive Monitoring: expressed agency
that defines and organizes assessment of the outcomes of a complex
intervention. '

P3. The production and
reproduction of a material practice requires continuous investment by agents in
ensembles of action that carry forward in time and space. From this follow specific
propositions that define actors’ investments in a mechanism (i.e. how the
mechanism is energized).

Technology
lifecycle

From Wikipedia, the free encyclopedia

Most new technologies follow a
similar technology maturity lifecycle describing the technological maturity of a product. This is
not similar to a product life cycle, but applies to an
entire technology, or a generation of a technology.

Technology adoption is the most
common phenomenon driving the evolution of industries along the industry
lifecycle. After expanding new uses of resources they end with
exhausting the efficiency of those processes, producing gains that are first
easier and larger over time then exhaustingly more difficult.

Technology
perception dynamics

There is usually technology
hype at the introduction of any new technology, but only after some time
has passed can it be judged as mere hype or justified true acclaim. Because of
the logistic
curve nature of technology adoption, it is difficult to see in the early
stages whether the hype is excessive.

The two errors commonly committed
in the early stages of a technology's development are[citation needed]:

fitting an exponential curve to the first
part of the growth curve, and assuming eternal exponential growth

fitting a linear curve to the first part of
the growth curve, and assuming that takeup of the new technology is
disappointing

Technology adoption typically occurs
in an S curve, as modelled in diffusion of innovations theory. This is
because customers respond to new products in different ways. Diffusion of innovations theory, pioneered
by Everett
Rogers, posits that people have different levels of readiness for adopting
new innovations and that the characteristics of a product affect overall
adoption. Rogers classified individuals into five groups: innovators, early
adopters, early majority, late majority, and laggards. In terms of the S curve,
innovators occupy 2.5%, early adopters 13.5%, early majority 34%, late majority
34%, and laggards 16%.

Stages

From a layman's perspective, the
technological maturity can be broken down into five distinct stages.

1.Bleeding
edge - any technology that shows high potential but hasn't demonstrated its
value or settled down into any kind of consensus. Early adopters may win big,
or may be stuck with a white elephant.

2.Leading edge
- a technology that has proven itself in the marketplace but is still new
enough that it may be difficult to find knowledgeable personnel to implement or
support it.

3.State
of the art - when everyone agrees that a particular technology is the right
solution.

4.Dated -
still useful, still sometimes implemented, but a replacement leading edge
technology is readily available.

5.Obsolete -
has been superseded by state-of-the-art technology, maintained but no longer
implemented.

Tipping point (sociology)

From Wikipedia, the free encyclopedia

In sociology, a tipping
point or angle
of repose is the event of a previously rare phenomenon becoming rapidly and
dramatically more common. The phrase was coined in its sociological use by Morton
Grodzins, by analogy with the fact in physics that
adding a small amount of weight to a balanced object can cause it to suddenly
and completely topple.

Grodzins studied integrating
American neighborhoods in the early 1960s. He discovered that most of the white families remained in the neighborhood as long as the comparative number of black families remained very small. But, at a certain point, when "one too
many" black families arrived, the remaining white families would move out en
masse in a process known
as white
flight. He called that moment the "tipping point". The idea was
expanded and built upon by Nobel Prize-winner Thomas
Schelling in 1972. A similar idea underlies Mark
Granovetter's threshold model of collective behavior.

Other
uses

The phrase has extended beyond
its original meaning and been applied to any process in which, beyond a certain
point, the rate at which the process proceeds increases dramatically. It has
been applied in many fields, from economics to human
ecology[1] to epidemiology. It can also be compared to phase
transition in physics or the propagation of populations in an unbalanced ecosystem.

From Wikipedia, the free encyclopedia

The angle of repose (sometimes incorrectly confused with the
'Angle of Internal Friction') is an engineering property of granular materials. The angle of repose is the
maximum angle of a stable slope determined by friction, cohesion and the shapes
of the particles.

When bulk granular materials are
poured onto a horizontal surface, a conical pile will form. The internal angle between the surface of the pile and the
horizontal surface is known as the angle of repose and is related to the density, surface
area, and coefficient of friction of the material.
Material with a low angle of repose forms flatter piles than material with a
high angle of repose. In other words, the angle of repose is the angle a pile
forms with the ground.

Applications of theory

The angle of repose is sometimes
used in the design of equipment for the processing of particulate solids. For
example, it may be used to design an appropriate hopper or silo to store the
material. It can also be used to size a conveyor
belt for transporting the material. It can also be used in determining
whether or not a slope (of a stockpile, or uncompacted gravel bank, for
example) will likely collapse; the talus slope is derived from angle of repose
and represents the steepest slope a pile of granular material will take. This
angle of repose is also crucial in determining the correct calculus of stability in vessels.

Measurement

There are numerous methods for
measuring angle of repose and each produces slightly different results. Results
are also sensitive to the exact methodology of the experimenter. As a result,
data from different labs is not always comparable.

An alternative measurement,
useful for many of the same purposes, is testing with a specialized instrument
called a shear cell.

Exploitation by
antlion larvae

Sand pit trap of
the antlion

The larva of the antlion traps
small insects such as ants by digging a conical pit in loose sand, such that
the slope of the walls is very close to the angle of repose for the sand.[1] Thus, when a small insect blunders into the pit, its
weight causes the sand to collapse below it, drawing the ant toward the center
where the antlion larva lies in wait. The antlion larva assists this process by
vigorously flicking sand out from the center of the pit when it detects a
disturbance, undermining the pit walls and causing them to collapse toward the
center, bringing the prey with them.

Catastrophe theory

Bifurcation theory studies and
classifies phenomena characterized by sudden shifts in behavior arising from
small changes in circumstances, analysing how the qualitative nature of equation solutions depends on the parameters that appear in the
equation. This may lead to sudden and dramatic changes, for example the
unpredictable timing and magnitude of a landslide.

Catastrophe theory, which
originated with the work of the French mathematician René Thom in the 1960s, and became very popular due to the efforts of Christopher Zeeman in the 1970s, considers the
special case where the long-run stable equilibrium can be identified with the
minimum of a smooth, well-defined potential function (Lyapunov function).

Small changes in certain
parameters of a nonlinear system can cause equilibria to appear or disappear,
or to change from attracting to repelling and vice versa, leading to large and
sudden changes of the behaviour of the system. However, examined in a larger
parameter space, catastrophe theory reveals that such bifurcation points tend
to occur as part of well-defined qualitative geometrical structures.

Hundredth Monkey Effect

From Wikipedia, the free encyclopedia

The “Hundredth Monkey Effect” is a supposed phenomenon in which a learned behaviour spreads instantaneously from one group of monkeys
to all related monkeys once a critical number is reached. By generalisation it
means the instant, paranormal spreading of an idea or ability to the remainder
of a population once a certain portion of that population has heard of the new
idea or learned the new ability. The story behind this supposed phenomenon
originated with Lawrence Blair and Lyall Watson, who claimed that it was the
observation of Japanese scientists. One of the primary factors in the
promulgation of the myth is that many authors quote secondary, tertiary or
post-tertiary sources who have themselves misrepresented the original
observations.

Popularization of the claim

The story of the “Hundredth
Monkey Effect” was published in the foreword to Lawrence Blair's Rhythms of
Vision in 1975.[1] The claim spread with the appearance of Lifetide, a 1979 book by Lyall
Watson. In it, Watson repeats Blair's claim. The authors describe similar
scenarios. They state that unidentified scientists were conducting a study of macaques monkeys on the Japanese island of Koshima in 1952.[2] These scientists purportedly observed that some of these
monkeys learned to wash sweet potatoes, and gradually this new behavior spread
through the younger generation of monkeys—in the usual fashion, through
observation and repetition. Watson then claimed that the researchers observed
that once a critical number of monkeys was reached—the so-called
hundredth monkey—this previously learned behavior instantly spread across
the water to monkeys on nearby islands.

This story was further
popularized by Ken Keyes, Jr. with the publication of his book The
Hundredth Monkey. Keyes
presented the “Hundredth Monkey Effect” story as an inspirational parable, applying
it to human society and the effecting of positive change therein. Since then,
the story has become widely accepted as fact and even appears in books written
by some educators.

The content of the book by Keyes
was a substantive treatise on the effects of nuclear war on the planet and the
devastation caused thereon. This idea has been debunked in several double-blind
studies and major universities.[citation needed]

The original
research

In 1985, Elaine
Myers re-examined the original published research in “The Hundredth
Monkey Revisited” in the journal In Context. In her review she found that the original
research reports by the Japan
Monkey Center in Vol. 2, 5, and 6 of the journal Primates are insufficient to support Watson’s story. In
short, she is suspicious of the existence of “Hundredth Monkey” phenomenon; the
published articles describe how the sweet potato washing behavior gradually
spread through the monkey troop and became part of the set of learned behaviors
of young monkeys, but she doesn’t agree that it can serve as an evidence for
the existence of a critical number at which the idea suddenly spread to other islands.

The effect
discredited

An analysis of the appropriate
literature by Ron
Amundson, published by the Skeptics
Society, revealed several key points that demystified the supposed effect.

Unsubstantiated claims that there
was a sudden and remarkable increase in the proportion of washers in the first
population were exaggerations of a much slower, more mundane effect. Rather
than all monkeys mysteriously learning the skill it was noted that it was
predominantly younger monkeys that learned the skill from the older monkeys
through the usual means of imitation; older monkeys who did not know how to
wash tended not to learn. As the older monkeys died and younger monkeys were
born the proportion of washers naturally increased. The time span between
observations were in the order of years.

Claims that the practice spread
suddenly to other isolated populations of monkeys ignore the fact that at least
one washing monkey swam to another population and spent about four years there.[citation needed] It is also to be noted that
the sweet potato was not available to the monkeys prior to human intervention:
it is not at all surprising that isolated populations of monkeys started to
wash potatoes in a similar time frame once they were made available.

Cultural references

This phenomenon is referenced in
the comic Y: The Last Man and is suggested to be related to the phenomenon
that is at the core of the series (the sudden, simultaneous death of almost
every male mammal on the planet).

Karl Pilkington mentioned it in
one of his monkey news items on the Ricky Gervais Show, XFM, on the 16th of August 2003.

Network effect

In economics and business, a network
effect (also called network
externality) is the
effect that one user of a good or service has on the value of that product to other people.

The classic example is the
telephone. The more people own telephones, the more valuable the telephone is
to each owner. This creates a positive externality because a user may purchase their phone without intending to create value for
other users, but does so in any case.

The expression "network
effect" is applied most commonly to positive network externalities as in
the case of the telephone. Negative network externalities can also occur, where
more users make a product less valuable, but are more commonly referred to as
"congestion" (as in traffic congestion or network congestion).

Origins

Network effects were a central
theme in the arguments of Theodore Vail, the first post patent president of Bell
Telephone, in gaining a monopoly on telephone services. In 1908, when he
presented the concept in Bell's annual report, there were over 4000 local and
regional telephone exchanges, most of which were eventually merged into the
Bell System. The economics of network effects were presented in a paper by Bell
employee N. Lytkins in 1917, where the term network externality was used.[citation needed]

Network effects were more
recently popularized by Robert Metcalfe, the founder of Ethernet. In
selling the product, Metcalfe argued that customers needed Ethernet cards to
grow above a certain critical mass if they were to reap the benefits of their
network. [1]

According to Metcalfe, the
rationale behind the sale of networking cards was that (1) the cost of cards
was proportional to the number of cards installed, but (2) the value of the
network was proportional to the square of the number of users. This was
expressed algebraically as having a cost of N, and a value of N². While
the actual numbers behind this definition were never firm, the concept allowed
customers to share access to expensive resources like disk drives and printers,
send e-mail, and access the internet.

Benefits

Network effects become
significant after a certain subscription percentage has been achieved, called critical mass. At the critical mass
point, the value obtained from the good or service is greater than or equal to
the price paid for the good or service. As the value of the good is determined
by the user base, this implies that after a certain number of people have
subscribed to the service or purchased the good, additional people will
subscribe to the service or purchase the good due to the positive utility:price ratio.

A key business concern must then
be how to attract users prior to reaching critical mass. One way is to rely on
extrinsic motivation, such as a payment, a fee waiver, or a request for friends
to sign up. A more natural strategy is to build a system that has enough value without network effects, at least to early
adopters. Then, as the number of users increases, the system becomes even
more valuable and is able to attract a wider user base. Joshua
Schachter has explained that he built Del.icio.us along these lines - he built an online system where he could keep bookmarks for
himself, such that even if no other user joined, it would still be valuable to
him.[2] It was relatively easy to build up a user base from zero
because early adopters found enough value in the system outside of the network
aspects. The same could be said for many other successful websites which derive
value from network effects, e.g. Flickr, MySpace.

Beyond critical mass, the
increasing number of subscribers generally cannot continue indefinitely. After
a certain point, most networks become either congested or saturated, stopping
future uptake. Congestion occurs due to overuse. The applicable analogy is that
of a telephone network. While the number of users is below the congestion
point, each additional user adds additional value to every other customer.
However, at some point the addition of an extra user exceeds the capacity of
the existing system. After this point, each additional user decreases the value
obtained by every other user. In practical terms, each additional user
increases the total system load,
leading to busy signals, the inability to get a dial tone,
and poor customer support. The next critical point is where
the value obtained again equals the price paid. The network will cease to grow
at this point, and the system must be enlarged. The congestion point may be larger than the market size. New Peer-to-peer technological models may always defy congestion. Peer-to-Peer systems, or
"P2P," are networks designed to distribute load among their user
pool. This theoretically allows true P2P networks to scale indefinitely. The
P2P based telephony service Skype benefits greatly from this effect. But market
saturation will still occur.

The network effect has a lot of
similarities with the description of phenomenon in reinforcing positive
feedback loops description of system
dynamics (Sterman 2000). System dynamics could be used as a modeling method
to describe such phenomenon such as word of
mouth and Bass model of marketing.

Business examples

Financial exchanges

Stock
exchanges and derivatives exchanges feature a network effect. Market
liquidity is a major determinant of transaction cost in the sale or
purchase of a security, as a bid-ask
spread exists between the price at which a purchase can be done versus the
price at which the sale of the same security can be done. As the number of
buyers and sellers on an exchange increases, liquidity increases, and
transaction costs decrease. This then attracts a larger number of buyers and
sellers to the exchange. See, for example, the work of Steve Wunsch (1999).[3]

The network advantage of
financial exchanges is apparent in the difficulty that startup exchanges have
in dislodging a dominant exchange. For example, the Chicago Board of Trade has retained
overwhelming dominance of trading in US Treasury
Bond futures despite the startup of Eurex US trading of
identical futures contracts. Similarly, the Chicago Mercantile Exchange has
maintained a dominance in trading of Eurobond interest rate futures despite a
challenge from Euronext.Liffe.

Software

There are very strong network
effects operating in the market for widely used computer software.

Take for example Microsoft
Office. For many people choosing an office
suite, prime considerations include how valuable having learned that office
suite will prove to potential employers, and how well the software interoperates with
other users. That is, since learning to use an office suite takes many hours,
they want to invest that time learning the office suite that will make them
most attractive to potential employers (or consulting clients, etc),
and they also want to be able to share documents. (Additionally, an example of
an indirect network effect in this case is the notable similarity in user-interfaces and operability menus of most new software - since that similarity directly
translates into less time spent learning new environments, therefore
potentially greater acceptance and adoption of those products.)

Similarly, finding
already-trained employees is a big concern for employers when deciding which office suite to purchase or
standardize on. The lack of cross-platformuser-interface standards results in a situation in which one firm is in control of almost 100%
of the market.

Microsoft
Windows is a further example of network effect. The most-vaunted advantage
of Windows, and that most publicised by Microsoft, is that Windows is
compatible with the widest range of hardware and software. Although this claim
was justified at some point of time, it was in reality the result of network
effect: hardware and software manufacturers ensure that their products are
compatible with Windows in order to have access to the large market of Windows
users. Thus, Windows is popular because it is well supported, but is well
supported because it is popular. However, network effects need not lead to
market dominance by one firm, when there are standards which allow multiple
firms to interoperate, thus allowing the network externalities to benefit the
entire market. This is true for the case of x86-based personal
computerhardware,
in which there are extremely strong market pressures to interoperate with
pre-existing standards, but in which no one firm dominates in the market. The
same holds true for the market for long-distance telephone service within the United States. In fact, the existence of these types
of networks discourages dominance of the market by one company, as it creates
pressures which work against one company attempting to establish a proprietary
protocol or to even distinguish itself by means of product differentiation.

In cases in which the relevant
communication protocols or interfaces are closed standards the network effect
can give the company controlling those standards monopoly power. The Microsoft corporation is widely seen by computer professionals as maintaining its
monopoly through these means. One observed method Microsoft uses to put the
network effect to its advantage is called embrace and extend (derisively called embrace, extend, and extinguish).

Mirabilis is an Israeli start-up
which pioneered instant messaging (IM) and was bought by America Online. By giving
away their ICQ product for
free and preventing interoperability between their client software and
other products, they were able to temporarily dominate the market for instant
messaging. Because of the network effect, new IM users gained much more value
by choosing to use the Mirabilis system (and join its large network of users)
than they would using a competing system. As was typical for that era, the
company never made any attempt to generate profits from their dominant position
before selling the company.

Web sites

Many web sites also feature a network effect. One example is web marketplaces and exchanges,
in that the value of the marketplace to a new user is proportional to the
number of other users in the market. For example, eBay would not be a
particularly useful site if auctions were not competitive. However, as the number of
users grows on eBay, auctions grow more competitive, pushing up the prices of
bids on items. This makes it more worthwhile to sell on eBay and brings more
sellers onto eBay, which drives prices down again as this increases supply,
while bringing more people onto eBay because there are more things being sold
that people want. Essentially, as the number of users of eBay grows, prices
fall and supply increases, and more and more people find the site to be useful.

The collaborative encyclopedia Wikipedia also benefits from a network effect. The theory goes that as the number of
editors grows, the quality of information on the website improves, encouraging
more users to turn to it as a source of information; some of the new users in
turn become editors, continuing the process.

Social
networking websites are also good examples. The more people register onto a
social networking website, the more useful the website is to its registrants.

By contrast, the value of a news
site is primarily proportional to the quality of the articles, not to the
number of other people using the site. Similarly, the first generation of
search sites experienced little network effect, as the value of the site was
based on the value of the search results. This allowed Google to win users
away from Yahoo! without much trouble, once users believed that Google's search results were
superior. Some commentators mistook the value of the Yahoo! brand (which does
increase as more people know of it) for a network effect protecting its
advertising business.

Alexa
Internet uses a technology that tracks users' surfing patterns; thus
Alexa's Related Sites results improve as more users use the technology. Alexa's
network relies heavily on a small number of browser software relationships,
which makes the network more vulnerable to competition.

Google has also attempted to
create a network effect in its advertising business with its Google AdSense
service. Google AdSense places ads on many small sites, such
as blogs, using
Google technology to determine which ads are relevant to which blogs. Thus, the
service appears to aim to serve as an exchange (or ad network) for matching
many advertisers with many small sites (such as blogs). In general, the more
blogs Google AdSense can reach, the more advertisers it will attract, making it
the most attractive option for more blogs, and so on, making the network more
valuable for all participants.

Network effects were used as
justification for some of the dot-combusiness
models in the late 1990s. These firms operated under the belief that when a
new market comes
into being which contains strong network effects, firms should care more about
growing their market share than about becoming profitable. This
was believed because market share will determine which firm can set technical
and marketing standards and thus determine the basis of future competition.

Rail gauge

There are strong network effects
in the initial choice of rail gauge, and in gauge
conversion decisions. Even when placing isolated rails not connected to any
other lines, track layers usually choose a standard rail gauge so they can use off-the-shelf
rolling stock. Although a few manufacturers make rolling stock that can adjust
to different rail gauges, most manufacturers make rolling stock that only works
with one of the standard rail gauges.

Technology
lifecycle

If some existing technology or
company whose benefits are largely based on network effects starts to lose
market share against a challenger such as a disruptive technology or open
standards based competition, the benefits of network effects will reduce
for the incumbent, and increase for the challenger.

In this model, a tipping
point is eventually reached at which the network effects of the challenger
dominate those of the former incumbent, and the incumbent is forced into an
accelerating decline, whilst the challenger takes over the incumbent's former
position.

Network effects are a source of,
but distinct from, lock-in. Lock-in can result from network effects, and
network effects generate increasing returns that are associated with lock-in.
However, the presence of a network effect does not guarantee that lock-in will
result. For example, if the network standards are open, enabling competitive
implementation by different vendors, there is no vendor lock-in.

Types of network
effects

There are two kinds of economic
value to be concerned about when thinking of network effects:

Inherent — I derive value
from my use of the product
Network — I derive value from other people's use of the product

Network value itself can be
direct or indirect.

Direct network value is an
immediate result of other users adopting the same system. Some examples of this
are fax machines and email.

Indirect is a secondary result of
many people using the same system. For example, complementary goods are cheaper
or more available when many people adopt a standard. Toner may be cheaper for widely
used printers. An example of this is that Windows and Linux can be seen as
competing not for users, but for software developers, as shown by Nicholas
Economides and Evangelos Katsamakas.

Negative and
positive network effects

Negative network effects result
from resource limits. Consider the connection that overloads the freeway
— or the competition for bandwidth. In fact, the automobile and ethernet congestion examples illustrate that there can be threshold limits. In this
case, the n+1 person begins to decrease the value of a network if additional
resources are not provided.

The result is that in some
networks there is an exclusion value. This is clear to anyone who has
considered problems of authentication or trust on the modern internet.

Another negative network effect
is provider complacency. The absence of viable competitors in a successful
network can cause a provider to restrict resources, consider fee increases, or
otherwise create an environment contrary to the users' benefit. These
situations are typically accompanied by vocal complaints from the users. (In a
competitive environment the users would simply change vendors rather than
complain.)

Path-dependenceexplains how the set of decisions one faces for any
given circumstance is limited by the decisions one has made in the past, even
though past circumstances may no longer be relevant.[1]

The phrase is
regularly used to mean one of two things (Pierson 2004):

Some authors use
path-dependence to mean simply "history matters" - a broad
concept;

Others use it to mean that
institutions are self reinforcing - a narrow concept.

It is the narrow
concept that has the most explanatory force and of which the discussions below
are examples. The claim "history matters" is trivially true and
reduces simply to "everything has causes".

…Consider as an
example the technological development of videocassette recorders (VCRs) for home use. It is argued that management errors and minor
design choices by Sony was one of the reasons why its Betamax format was defeated in market competition by VHS in the 1980s. Two mechanisms
can explain why the small but early lead gained by VHS became larger over time.
The first is the bandwagon effect of VCR manufacturers in favor of
the VHS format in the U.S. and Europe, who switched because they expected VHS
to win the standards battle. The second was a network
effect: videocassette rental stores observed that more people had VHS
players and stocked up on VHS tapes; this in turn led other people to buy VHS
players, and so on until there was complete vendor
lock-in to VHS. An alternative explanation, of course, is that VHS was
better adapted to market demands (in particular to the demand for longer
cassettes for recording sports games) and that path dependence had little or
nothing to do with its success. There is also some support for this latter
claim.

Positive
feedback mechanisms like bandwagon and network effects are at the origin of
path-dependence. They lead to a reinforcing pattern, in which industries 'tip'
towards one or another product design. Uncoordinated standardisation can be observed in many other situations.

Examples from
economics, history, software, and biology are presented below.

Metcalfe's law

Two
telephones can make only one connection, five can make 10 connections, and
twelve can make 66 connections.

The law has often
been illustrated using the example of fax machines: a single fax machine is useless, but the value of
every fax machine increases with the total number of fax machines in the
network, because the total number of people with whom each user may send and
receive documents increases.

Metcalfe's law is
more of a heuristic or metaphor than an iron-clad empirical rule. In addition to the difficulty of
quantifying the "value" of a network, the mathematical justification
measures only the potential number of contacts, i.e., the technological side of a
network. However the social utility of a network depends upon the number of
nodes in contact. For instance, if Chinese and non-Chinese users don't understand each
other, the utility of a network of users that speak the other language is near
zero, and the law has to be calculated for the two sub-networks separately.

From
Wikipedia, the free encyclopedia

The reason for
this is that the number of possible sub-groups of network participants is , where N is the number of participants. This grows much more rapidly than either

the number of participants, N, or

the number of possible pair
connections, (which follows Metcalfe's
law)

so that even if
the utility of groups available to be joined is very small on a peer-group
basis, eventually the network effect of potential group membership can
dominate the overall economics of the system.

[Note: an earlier
version of this essay was prepared as an online supplement to an article in Context magazine published in Spring
1999]

Bob Metcalfe, inventor of the Ethernet, is known for pointing out that the
total value of a communications network grows with the square of the number of
devices or people it connects. This scaling law, along with Moore's Law, is
widely credited as the stimulus that has driven the stunning growth of Internet
connectivity. Because Metcalfe's law implies value grows faster than does the
(linear) number of a network's access points, merely interconnecting two
independent networks creates value that substantially exceeds the original
value of the unconnected networks. Thus the growth of Internet connectivity,
and the openness of the Internet, are driven by an inexorable economic logic,
just as the interconnection of the telephone network was forced by AT&T's
long distance strategy. This strategy created huge and increasing value to
AT&T customers, based on the same (then unnamed) law of increasing returns
to scale at the beginning of the 20th century. In the same way, the global
interconnection of networks we call the Internet has created huge and
increasing value to all its participants.

Conventional wisdom is that a remarkably powerful effect known as
Metcalfe's Law is driving the growth of the Internet. The law says that the
value of a network grows in proportion to the square of the number of users,
which means that, once a network achieves a certain size, it becomes almost
irresistibly attractive. But Metcalfe's Law actually understates the potential
value of the Internet, and by a huge margin.

I'd like to suggest a new
way of looking at the economics of the Internet. I think my approach can
explain why forecasters have so consistently underestimated its growth. (And,
believe me, they have: In 1995, estimates for on-line commerce in 1998 were $2
billion to $3 billion, while the real number turned out to be more like $13
billion.) My approach not only should add to the urgency that businesses feel
about moving on-line but also helps identify which on-line strategies will work
and which will fail.

It helps to first understand
the two laws of networks that have been around for some time. First is what
might be called Sarnoff's Law, after the pioneer of the broadcast industry.
This law says that the value of a network grows in proportion to the number of
viewers.

Second is the law named
after Bob Metcalfe, the inventor of the Ethernet computer-networking
technology. He reasoned that 1,000 people on a network can have roughly one
million different conversations, so he said the value of a network grows in
proportion to the square of the number of users. The n² value explains the
growth behavior of networks, such as phone systems or electronic-mail systems,
that are mainly used for one-on-one communication. The n² effect says that,
given the choice of joining a large existing network with many users or an
incompatible new one with few users, new users will almost always decide that
the bigger one is far more valuable. The result is often explosive,
accelerating growth once a network establishes dominance. This behavior
explains why there is now one global e-mail system, while just a few years ago
there were thousands.

There's an additional law at
play with the Internet because it facilitates the formation of groups, in a way
that Sarnoff and Metcalfe networks do not. The number of groups that can be
formed over the Internet isn't the Sarnoff n or Metcalfe n². It's 2n by the time you add up all the possible two-person groups, three-person groups,
etc. So, the value of the Internet grows in proportion to 2n. Let's
call this effect the Group-Forming Law.

This law is so powerful
because 2n gets impossibly large very fast. There's the old story
about the king who rewarded a wise minister by offering him anything he wanted.
The minister said all he wished for was two copper coins on the first square of
a chess board, four on the second, eight on the third, and so on—a
progression based on 2n. The king protested that the minister should
ask for gold or pearls, not copper. But, by the time the 8,192 coins were
placed on the 13th square, the king realized he'd been had—264;
is more than 18 quintillion, which, if memory serves, is more grains of sand
than exist in the world. (The story has it that the king had the minister
beheaded for being a wiseguy.)

All three laws, in fact,
apply to the Internet. Services such as news sites that are aimed at
individuals benefit from additional users in a linear, Sarnoff way. Services
aimed at facilitating transactions, such as many commercial sites, benefit in
an n² Metcalfe way. Services aimed at building communities, such as AOL,
benefit in a 2n, Group-Forming way. What's important is that the
dominant value in a typical network tends to shift from Sarnoff to Metcalfe to
Group-Forming as the scale of the network increases. So, as the Internet
continues to expand, investments in Group-Forming networks are likely to
produce the biggest returns.

As the scale increases,
what's important also shifts. When Sarnoff's Law dominates, content such as TV
programs is king. When Metcalfe's Law kicks in, transactions are king. When the
Group-Forming Law takes hold, communities are king. The value in a
Group-Forming network is constructed jointly, whether through discussion
groups, through joint plans to buy something in bulk at low prices, or through
some other means.

But the
theory is less important than the practice, at least if you're trying to profit
from the Internet, so I'll make some predictions based on the likely effects of
the Group-Forming Law: MORE

The Long Tail

An
example of a power law graph being used to demonstrate ranking of popularity.
To the right is the long tail, to the left are the few that dominate (also
known as the 80-20 rule).

The phrase The
Long Tail (as a proper noun) was first coined by Chris Anderson in an October 2004 Wired magazine article[1] to describe the niche strategy of businesses, such as Amazon.com or Netflix,
that sell a large number of unique items, each in relatively small quantities.

A frequency distribution with a long tail
— the concept at the root of Anderson's coinage — has been studied
by statisticians since at least 1946.[2] The distribution and inventory costs of these businesses
allow them to realize significant profit out of selling small volumes of
hard-to-find items to many customers, instead of only selling large volumes of
a reduced number of popular items. The group that purchases a large number of
"non-hit" items is the demographic called the Long Tail.

Given a large
enough availability of choice, a large population of customers, and negligible
stocking and distribution costs, the selection and buying pattern of the
population results in a power law distribution curve, or Pareto distribution. This suggests that a
market with a high freedom of choice will create a certain degree of inequality
by favoring the upper 20% of the items ("hits" or "head")
against the other 80% ("non-hits" or "long tail").[3]

Ken
McCarthy addressed this phenomenon from the media producers' point of view
in 1994. Explaining that the pre-Internet media industry made its distribution
and promotion decisions based on what he called lifeboat economics and not on quality or even
potential lifetime demand, he laid out a vision of the impact he expected the Internet and
consumer choice would have on the structure of the media
industry, foreshadowing many of the ideas that appeared in Chris Anderson's
book The Long Tail: Why the Future of Business Is Selling Less of More (ISBN
1-4013-0237-8).

The Long Tail
concept has found a broad ground for application, research and experimentation.
It is a common term in the online business and mass media, but also of
importance in micro-finance (see Grameen Bank), user-driven innovation (Eric
von Hippel), social network mechanisms (e.g., crowdsourcing, crowdcasting, Peer-to-peer), economic models, and marketing (viral
marketing).

Business model

A business
model is
a framework for creating economic, social, and/or other forms of value. The
term business model is thus used for a broad range of informal and formal
descriptions to represent core aspects of a business, including purpose,
offerings, strategies, infrastructure, organizational structures, trading
practices, and operational processes and policies.

In the most basic
sense, a business model is the method of doing business by which a company can
sustain itself -- that is, generate revenue. The business model spells-out how
a company makes money by specifying where it is positioned in the value chain.

Conceptualization

Conceptualizations
of business models try to formalize informal descriptions into building blocks
and their relationships[1]. While many different conceptualizations exist[2][3][4][5][6][7][8][9], Osterwalder proposed[10] a synthesis of different conceptualizations into a single
reference model based on the similarities of a large range of models, and constitutes a business
model design template which allows enterprises to describe their business
model:

Business
model design template: Nine building blocks and their relationships,
Osterwalder 2004[10]

Infrastructure

Core capabilities: The
capabilities and competencies necessary to execute a company's business
model.

Partner network: The business alliances which complement other
aspects of the business model.

Value configuration: The
rationale which makes a business mutually beneficial for a business and
its customers.

Offering

Value proposition: The products and services
a business offers. Quoting Osterwalder (2004), a value proposition
"is an overall view of .. products and services that together
represent value for a specific customer segment. It describes the way a
firm differentiates itself from its competitors and is the reason why
customers buy from a certain firm and not from another."

Customers

Target customer: The target
audience for a business' products and services.

Distribution channel: The
means by which a company delivers products and services to customers. This
includes the company's marketing and distribution strategy.

Customer relationship: The
links a company establishes between itself and its different customer
segments. The process of managing customer relationships is referred to as customer relationship management.

Finances

Cost structure: The monetary
consequences of the means employed in the business model. A company's DOC.

Revenue: The way a company
makes money through a variety of revenue flows. A company's income.

Evolution

A brief history
of the development of business models might run as follows. The oldest and most
basic business model is the shopkeeper model. This involves setting up a store
in a location where potential customers are likely to be and displaying a product or service.

Over the years,
business models have become much more sophisticated. The bait and hook business model (also
referred to as the "razor and blades business model"
or the "tied products business model") was introduced in the early
20th century. This involves offering a basic product at a very low cost, often
at a loss (the "bait"), then charging compensatory recurring amounts
for refills or associated products or services (the "hook"). Examples
include: razor (bait) and blades (hook); cell phones (bait) and air time
(hook); computer printers (bait) and ink cartridge refills (hook); and cameras
(bait) and prints (hook). An interesting variant of this model is a software
developer that gives away its word processor reader for free but charges
several hundred dollars for its word processor writer.

Today, the type
of business models might depend on how technology is used. For example,
entrepreneurs on the internet have also created entirely new models that depend
entirely on existing or emergent technology. Using technology, businesses can
reach a large number of customers with minimal costs.

Importance

Malone et al.[11] at MIT find that some business models, as defined by
them, indeed performed better than others in a dataset consisting of the
largest U.S. firms, in the period 1998 through 2002, while they did not prove
whether the existence of a business model mattered.

Perhaps the most
overlooked dimension in developing a business model especially for a new
product/service/business is the dimension of time, more specifically the timing
of investments/expenses or cash flow out versus the receipt of
revenues/accounts receivables or cash flow in. The principle issues are: 1)
Essentially how much of the product or service has to be built before customers
can make some level of either actual purchase decision and/or purchase
commitment? 2) How much investment/expense is required to secure these
revenues/commitments from customers? and 3 )How much risk is there in achieving
net positive cash flow, given the required upfront investment and the future
time to capture revenues/receivables cash inflow, within an acceptable
timeframe, if ever?

These business
model issues often make or break new ventures. Business models that are
optimized to reduce the upfront investment, that accelerate the
revenue/receivables cash inflow, that obtain cogent and reliable customer
feedback often and earlier, and that take other measures to reduce the
investment risk all have a higher probability of business success.

For example, in
the entertainment industry, does one have to produce a movie for $100 million
plus before any box office revenues can be derived, or can the business model
be evolved by licensing certain established characters/signing leading movie
stars for secondary licensing rights for fast-food chain promotional-tie-ins,
movie merchandise licenses, etc. can generate pre-release cash inflow through
licensing fees? Or a different entertainment business model might be to create
and promote a "Weirdest Video" website platform for users to
contribute the content and then based on site traffic, sell advertising for
revenues. Here, the upfront investment for creating and promoting the site
could be a fraction of the investment to produce a movie and the chances that
it would be more popular than a movie may be much higher, as it can be tweaked
as it is developed while a movie is an all-or-nothing production.

It comes down to
a nitty gritty question: Can we make to order or do we have to create a new
mousetrap and then wait to see if the world will come to it, or somewhere
in-between?

Related
Concepts

The process of
business model design is part of business
strategy. The implementation of a company's business model into
organizational structures (e.g. organigrams, workflows, human resources) and
systems (e.g. information technology architecture, production lines) is part of
a company's business operations. It is important to
understand that business modeling commonly refers to business
process design at the operational level, whereas business models and business model design refer to defining the
business logic of a company at the strategic level.